Face Reconstruction with Generative Adversarial Network

Abstract

Generative Adversarial Network (GAN) is a framework of deep learning in generative models. The generative model aims to synthesize a new data so that it has a distribution of distribution according to the original data distribution. In the current development, GAN is not only used to synthesize data from noise alone, but in the current development it has begun to be used to translate data from a domain to data with a different domain. Several studies have been developed, such as CycleGAN, and Pix2pix. In this study, the face has not been used as an object of translation. In this study a model for translating images of face sketches into face images will be made.